dLagM: An R package for distributed lag models and ARDL bounds testing

Abstract

In this article, we introduce the R package dLagM for the implementation of distributed lag models and autoregressive distributed lag (ARDL) bounds testing to explore the short and long-run relationships between dependent and independent time series. Distributed lag models constitute a large class of time series regression models including the ARDL models used for cointegration analysis. The dLagM package provides a user-friendly and flexible environment for the implementation of the finite linear, polynomial, Koyck, and ARDL models and ARDL bounds cointegration test. Particularly, in this article, a new search algorithm to specify the orders of ARDL bounds testing is proposed and implemented by the dLagM package. Main features and input/output structures of the dLagM package and use of the proposed algorithm are illustrated over the datasets included in the package. Features of dLagM package are benchmarked with some mainstream software used to implement distributed lag models and ARDLs.

Modeling of Logging Industry Dynamics Under the Global Climate Change: the Evidence from Siberian Regions

“…where a 0 is a constant, b 1 , b 2 , … b p and a 1 , a 2 , … a p -model parameters and e t -error term (Demirhan, 2020), L is a logging volume in Krasnoyarsk Krai or Irkutsk Oblast and S is a winter logging season duration on meteorological station located within the respective region.…”

Almon-KL estimator for the distributed lag model

“…This dataset is composed of monthly global mean sea level (GMSL) (compared to 1993-2008 average) series by CSIRO, land ocean temperature anomalies as a baseline period) by GISS, NASA, and monthly Southern Oscillation Index (SOI) by Australian Government Bureau of Meteorology (BOM) between July 1885 and June 2013 (Church & White, 2011;Demirhan, 2020). In this study, the response and the explanatory variables were chosen to be GMSL and land ocean temperature anomalies, respectively.…”

“…The dataset is available in the R package data (seaLevelTempSOI). More detail on the data description is available in the study of Demirhan (2020). The lag length is taken to be 4 and the lag weight approximated by a polynomial of order 2 (Demirhan, 2020).…”

“…More detail on the data description is available in the study of Demirhan (2020). The lag length is taken to be 4 and the lag weight approximated by a polynomial of order 2 (Demirhan, 2020). The eigenvalues of Z 0 Z are computed to be 6607.86, 1590.99, 67.12 and 6.96, respectively.…”

Effectiveness of fiscal federalism for poverty reduction in Nigeria: an analysis of federal and state governments’ expenditures

“…ARDL technique was employed because of its advantages over other cointegration techniques. These advantages include applicability irrespective of the order of integration of series, though order of integration should not be beyond order 1, useability with relatively small samples, and possibility of simultaneously estimating long-and short-run dynamics (Pesaran et al 2001;Ewetan et al 2020;Demirhan 2020).…”